Low Power Consumption Research Articles

Enhanced resistive switching performance in TiN/AlOx/Pt RRAM by high-temperature I-V cycling

Set voltage is a key parameter for the application of Resistance Random Access Memory (RRAM). In this paper, based on TiN/AlOx/Pt RRAM synthesized by the magnetron sputtering method, we have studied the influence of I-V cycling at high temperatures on resistive switching performance. The results show that the treatment can significantly reduce the set voltage in resistive switching cycles. Moreover, the treatment can also enhance endurance effectively. Further studies indicated that a higher compliance current in treatment can induce a smaller and more uniform set voltage. We ascribe the improvements in resistive switching performance to the generation and accumulation of oxygen vacancies in the treatment. This research provides new ideas for synthesizing RRAM devices with low power consumption.

Approximate single precision floating point adder for low power applications

With an increasing demand for power-hungry data-intensive computing, design methodologies with low power consumption are increasingly gaining prominence in the industry. Most of the systems operate on critical and noncritical data both. An attempt to generate a precision result results in excessive power consumption and results in a slower system. For noncritical data, approximate computing circuits significantly reduce the circuit complexity and hence power consumption. In this paper, a novel approximate single precision floating point adder is proposed with an approximate mantissa adder. The mantissa adder is designed with three 8-bit full adder blocks. In this paper, a detailed mathematical background, and proposed design approach in terms of the circuit configuration and truth tables are discussed. Additionally, a concept of switching between exact computing and approximate computing is analysed considering an approximate carry look-ahead adder. The delay and power consumption for the exact operating mode and approximate operation mode considering varied window sizes is observed. Performance of the approximate computation is compared against exact computation and varied approximate computing approaches.

Research on Techniques for Enhancing the Speed of Low-Power Operational Amplifier

In the context of low utilization, this paper explores various techniques for enhancing the speed of operational amplifier (OA). The main text delves into three methods to gain equilibrium: fully differential operational amplifier (FDA), twostage operational transconductance amplifier (OTA), and a novel high-speed calculation system architecture employing a “rhombus crystal tube.” The FDA improves speed by minimizing noise and enhancing bandwidth and output voltage swing, without increasing power consumption. The design of the two-stage OTA combines the characteristics of a differential amplifier and a telescopic amplifier, optimizing gain and slew rate through Miller compensation and noise gain manipulation, thereby achieving high-speed performance. A novel high-speed operational amplifier structure employs diamond transistors to supply substantial current for capacitor charging, enhancing the slew rate and overall speed. Throughout the text, these methods are presented as a means to jointly promote high speed, low power consumption, and the handling of a significant number of transistors. To further increase the speed, the size of the microcrystalline tube is crucial. The research direction is outlined, and while the design equipment is in the foreground, there remains room for progress, especially in applications for large-scale electrical models. Future research aims to enhance the power efficiency of circuits, making them more practical and efficient. This study provides valuable insights into balancing power consumption and speed in advanced electronic devices.

Joint Design of Transmit Waveform and Altitude for Unmanned Aerial Vehicle-Enabled Integrated Sensing and Wireless Power Transfer Systems

Recently, unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has received great attention as a promising technology for providing stable power to energy-constrained devices by navigating three-dimensional (3D) space, particularly in challenging environments such as maritime networks and smart cities. Additionally, UAV-enabled radar sensing has gained significant attention as a key technology for future 6G networks, as it enables high-accuracy sensing for various applications, such as target detection and tracking, surveillance, and environmental monitoring, as well as autonomous UAV operation. In this regard, we investigated UAV-enabled integrated sensing and wireless power transfer (ISWPT) systems that combine radar sensing and WPT operations on a unified hardware platform, sharing the same spectrum of resources. In order to accurately sense multiple targets and efficiently transfer power to multiple devices at the same time, we propose a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between radar sensing performance with the desired beam pattern and WPT performance with the desired harvested power of the devices. We first developed an effective method to obtain the optimal waveform and altitude by solving a challenging non-convex optimization problem. Based on this, we developed another efficient, low-complexity method by exploring a novel transmit waveform and optimizing its parameters to reduce computational complexity and thereby lower power consumption in UAVs. The numerical results verify that the proposed method significantly improves both radar sensing and WPT performance, as well as substantially reduces computational complexity.

A VOx based optoelectronic memristor for application in visual perception

Abstract While biological vision systems excel at in-memory processing with low power consumption, traditional silicon-based vision chips struggle with high energy demands. This gap motivates the exploration of alternative materials for artificial intelligence applications. This paper presents a VOx-based optoelectronic synaptic memristive device. The proposed artificial synaptic device ITO/VOx /Pt mimics biological functions such as potentiation (P), depression (D), long-term memory (LTM), short-term memory (STM), and paired-pulse facilitation (PPF). The PPF index, standing at 105%, suggests a favorable pattern in short-term memory function. The device served as synapses within a spiking neural network (SNN) showing an achievable pattern classification accuracy of 88.68%, highlighting the potential of the VOx synaptic device for pattern classification tasks. The proposed VOx-based synaptic devices could be a promising platform for efficient pattern recognition and visual perception applications.

Enhanced detection performance based on a differential ME sensor with strong suppression of vibration interference

Magnetoelectric (ME) sensors have enormous potential for detecting weak magnetic fields because of their high sensitivity, low power consumption, compact size and, low cost. However, inevitable vibration interference limits their application in practical environments, especially in the case of mobile platform mounting. Here, we propose a differential ME sensor, consisting of PZT macro-fiber composites (MFCs) and Metglas laminates. The differential ME sensor has two output terminals with weak mutual mechanical coupling and works in longitudinal vibration mode. MFC cores are polarized in parallel mode to guarantee their consistency of electric characteristics and reversed bias field is provided by attached magnets. Experimental results show that the differential-mode response amplitudes have a gain of −17.6 dB for low-frequency vibration at 2 Hz and ∼6.2 dB for an applied magnetic field at 3 Hz, in comparison with the single-ended mode. In addition, our proposed ME sensor also has a low inherent equivalent magnetic noise of 18.3 pT/√Hz at 1 Hz. Finally, a target detection experiment in the presence of heavy lab noise and strong vibration interference is conducted and the improved detection performance of the proposed differential ME sensor is proved.

Néel Vector Auto-Oscillations and Reorientations Induced by Spin-Polarized Electric Currents in Antiferromagnetic Mn2Au Nanolayer

The electrical excitation of spin dynamics in antiferromagnets is important for the development of high-speed spintronic devices with a low power consumption. Here, we present a theoretical study of the spin dynamics generated in the Ni/Cu/Mn2Au/Cu/Ni nanostructure by a perpendicular-to-plane electric current. Our investigation includes atomistic simulations of spin reorientations in a few monolayer antiferromagnetic Mn2Au film and numerical calculations of nonequilibrium spin accumulation in the nanostructure comprising ferromagnetic Ni polarizers with antiparallel magnetizations. This combined approach enables us to quantify the dynamics of Mn magnetic moments induced by the spin-transfer torque created by the spin-polarized charge flow. The calculations show that the direct electric current with the density exceeding some temperature-dependent threshold value gives rise to steady-state auto-oscillations of the Néel vector in the [Formula: see text]-oriented Mn2Au nanolayer. As the precession of Mn magnetic moments occurs around an out-of-plane direction strongly deflected from their initial in-plane orientations, the emergence of auto-oscillations should be regarded as the realization of a dynamic spin reorientation transition in the antiferromagnetic crystal. Remarkably, the precession frequency rises with increasing current density J and exceeds 1[Formula: see text]THz at [Formula: see text][Formula: see text]A[Formula: see text]m[Formula: see text], which makes the described antiferromagnetic spin transfer nano-oscillator attractive for device applications. Furthermore, our simulations demonstrate that a picosecond current pulse injected into the Ni/Cu/Mn2Au/Cu/Ni nanostructure can induce a precessional switching of the Néel vector. Depending on the current density and pulse duration, the Néel vector rotates by either 90° or 180° and attains stable orientation along the corresponding [Formula: see text] easy axis in the plane of the Mn2Au nanolayer. This feature shows that the Ni/Cu/Mn2Au/Cu/Ni nanostructure could be used as a nonvolatile memory cell with the electrical writing and readout.

Quantized neural network for complex hologram generation

Computer-generated holography (CGH) is a promising technology for augmented reality displays, such as head-mounted or head-up displays. However, its high computational demand makes it impractical for implementation. Recent efforts to integrate neural networks into CGH have successfully accelerated computing speed, demonstrating the potential to overcome the trade-off between computational cost and image quality. Nevertheless, deploying neural-network-based CGH algorithms on computationally limited embedded systems requires more efficient models with lower computational cost, memory footprint, and power consumption. In this study, we developed a lightweight model for complex hologram generation by introducing neural network quantization. Specifically, we built a model based on tensor holography and quantized it from 32-bit floating-point precision (FP32) to 8-bit integer precision (INT8). Our performance evaluation shows that the proposed INT8 model achieves hologram quality comparable to that of the FP32 model while reducing the model size by approximately 70% and increasing the speed fourfold. Additionally, we implemented the INT8 model on a system-on-module to demonstrate its deployability on embedded platforms and high power efficiency.

Secure low-cost photovoltaic monitoring system based on LoRaWAN network and artificial intelligence

The advanced study will focus on developing a secure and cost-effective photovoltaic monitoring system using Long Range Wide Area Network (LoRaWAN) and Artificial Intelligence technologies for highly risky missions. With the world shifting to using renewable energy, the development of secure and efficient cost-effective monitoring systems for photovoltaic installations becomes very important. However, most of the solutions in use for PV installation monitoring are characterized by high costs, hence their limited use, especially in developing regions. In this regard, the present work presents country-specific protocols designed for the country of Turkey. The proposed system in this study is expected to offer real-time monitoring and data collection at a very meager cost by utilizing inexpensive microcontrollers and sensors. The accuracy rate demonstrated by the research is highly above board: 97.65%. It costs $210, with a very low power consumption of 0.5W and a range of 10 km. The major features underpin real-time monitoring with wide area coverage for effectiveness and efficiency in PVs monitoring. Test results in many places around Turkey have turned out reliable and accurate to justify a possible reduction in operational costs without compromising the quality of data received. This design is reinforced by a detailed mathematical model that assures one of its thorough analysis in performance. There is custom-built firmware that enhances communication efficiency and ensures data integrity in implementation. Comparative studies carried out with existing technologies indicate that the proposed system is cost-effective and operationally sound. This work gives a scalable, low-cost solution that probably might raise the uptake of PV systems in economically constrained areas.

A Drosophila-inspired intelligent olfactory biomimetic sensing system for gas recognition in complex environments

The olfactory sensory system of Drosophila has several advantages, including low power consumption, high rapidity and high accuracy. Here, we present a biomimetic intelligent olfactory sensing system based on the integration of an 18-channel microelectromechanical system (MEMS) sensor array (16 gas sensors, 1 humidity sensor and 1 temperature sensor), a complementary metal‒oxide‒semiconductor (CMOS) circuit and an olfactory lightweight machine-learning algorithm inspired by Drosophila. This system is an artificial version of the biological olfactory perception system with the capabilities of environmental sensing, multi-signal processing, and odor recognition. The olfactory data are processed and reconstructed by the combination of a shallow neural network and a residual neural network, with the aim to determine the noxious gas information in challenging environments such as high humidity scenarios and partially damaged sensor units. As a result, our electronic olfactory sensing system is capable of achieving comprehensive gas recognition by qualitatively identifying 7 types of gases with an accuracy of 98.5%, reducing the number of parameters and the difficulty of calculation, and quantitatively predicting each gas of 3–5 concentration gradients with an accuracy of 93.2%; thus, these results show superiority of our system in supporting alarm systems in emergency rescue scenarios.

Nonvolatile logic inverters based on 2D CuInP2S6 ferroelectric field effect transistors

With the capability of in-memory computing, integrated nonvolatile logic devices can mitigate the back-and-forth movement of data between storage and logic units, thus effectively enhancing computational speed and reducing power consumption. In this work, two-dimensional (2D) CuInP2S6, which reveals robust polarization within a few nanometer thicknesses, was utilized as gate dielectric for ferroelectric field effect transistor (FeFET). Device with a ReS2 channel demonstrated a high on/off ratio of current of 105, accompanied by a substantial hysteresis window of 2.8 V. Additionally, ReS2 FETs, gated with an h-BN layer, exhibited a switch ratio of 108 and minimal hysteresis of 61.6 mV at room temperature, attributed to the atomically flat heterointerface with negligible traps. Leveraging the performance of these devices enabled the creation of a nonvolatile logic inverter, wherein the ReS2/h-BN FET acts as the load transistor, and ReS2/h-BN/CIPS FeFET serves as the driving unit. This configuration stably operates with low power consumption of 0.45 μW and outstanding retention exceeding 1000 s. This work presents a viable device architecture for designing nonvolatile logic circuits applicable in in-memory computing.

A Physical Memristor Model for Pavlovian Associative Memory

Abstract Brain-inspired intelligence is considered a computational model with the most promising potential to overcome the shortcomings of the von Neumann architecture, making it a current research hotspot. Due to advantages such as nonvolatility, high density, low power consumption, and high response ratio, memristors are regarded as devices with promising applications in brain-inspired intelligence. This paper proposes a physical Ag/HfO$_{x}$/FeO$_{x}$/Pt memristor model. Firstly, the Ag/HfO$_{x}$/FeO$_{x}$/Pt memristor is fabricated using magnetron sputtering, and its internal principles and characteristics are thoroughly analyzed. Furthermore, we constructed a corresponding physical memristor model which achieves a simulation accuracy of up to 99.72% for the physical memristor. We design a fully functional Pavlovian associative memory circuit, realizing functions including generalization, primary differentiation, secondary differentiation, and forgetting. Finally, the circuit is validated through PSPICE simulation and analysis.

Appropriate Selection of Frequency Range and Damping Ratio to Reduce the Resonance Effect, Lower Power Consumption, and Improve the Accuracy of the Capacitive Accelerometer

Introduction: In this work, we explored the capacitive accelerometer in depth by highlighting its advantages over other accelerometers. However, a major challenge associated with using capacitive accelerometers lies in choosing the appropriate frequency range. Method: Incorrect selection of the frequency range can cause several problems. It can decrease the accuracy of the accelerometer, increase the measurement error, and increase power consumption. To overcome these challenges, we undertook detailed modeling of the physical behaviour of the capacitive accelerometer. This modeling takes into account the mechanical and electrical properties of the sensor, including its mass, its rigidity, and the characteristics of its capacitive circuit. By simulating the developed model, we analyzed how the accelerometer reacts to different frequencies of vibratory movements. Result: This analysis led to the extraction of an important mathematical relationship that links the natural frequency of the accelerometer to the frequency of the vibratory movements to which it is subjected. Using this mathematical relationship, we determined the optimal frequency range for capacitive accelerometer operation. This precise determination of the frequency range makes it possible to significantly reduce the measurement error by avoiding resonance regimes and ensuring that the sensor operates in its maximum precision zone. Conclusion: Additionally, this approach helps improve overall measurement accuracy and minimize sensor power consumption by avoiding inefficient operating conditions.

The application and challenges of different face recognition technologies in the three major fields of security, social media, and medical care

Abstract. Face recognition technology is a very important part of modern technology, which can not only be used to ensure security, but also can be used in the field of information organization and content division. However, with the popularization and application of face recognition technology, many problems that need to be solved urgently have emerged: excessive computing resources are consumed in order to pursue high-precision recognition, which brings computing pressure; The basis for improving recall is the need for a lot of power and memory; If security is not guaranteed, it can cause problems such as data breaches. The demand for face recognition technology is different in different use fields, so the purpose of this study is to combine the scene requirements and technical advantages more reasonably. The research results are as follows: the high accuracy and recall rate of 3D convolutional neural networks (3DCNNs) ensure that it can be used safely in high-precision and high-security scenarios. Lightweight Convolutional Neural Network (MobileNetV2) is suitable for resource-constrained environments due to its low memory consumption and low communication cost. Edge computing real-time face recognition (EC-RFERNet) is the most suitable for large-scale popularization and application among the three because of its lowest power consumption and latency. This study deeply explores the advantages and disadvantages of different facial recognition technologies and finds solutions to their shortcomings. According to their unique advantages combined with the requirements of commonly used scenarios, it provides a scientific basis for the deployment of face recognition technology in different fields. However, due to limited information, this paper cannot cover all application scenarios and the latest technologies, so it is hoped that in the future, we can combine the advantages of different technologies to develop more comprehensive face recognition technology and make more reasonable technical planning.

A FinFET-based static memory cell optimized for stability and low power consumption

A FinFET-based static memory cell optimized for stability and low power consumption

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Lightweight skin cancer detection IP hardware implementation using cycle expansion and optimal computation arrays methods

Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands. This study proposes a lightweight hardware design based on a convolutional neural network (CNN) for real-time processing of skin disease classifiers on portable devices. Our skin cancer recognition processor utilizes an optimally parallel designed processing engine (PE) for global computation, which greatly reduces hardware resource utilization by multiplexing of computational unit circuits. In addition, a design approach that provides loop unrolling effectively reduces the number of data accesses, thereby reducing computational complexity and logic resource requirements. The hardware circuits in this design perform data inference in convolutional, pooling, and fully connected layers based on 16-bit floating-point numbers. Evaluation of the HAM10000 database dataset shows that the architecture achieves an average classification accuracy of 97.8 %. We are the first to implement an all-hardware FPGA-based skin cancer detection platform that offers a 3.5x speedup in recognition compared to existing skin cancer accelerators at 50 MHz while consuming only 0.48 W of power. The implementation of this hardware architecture meets the major constraints of portable devices, featuring low resource utilization, low power consumption, and cost-effectiveness, while still providing efficient classification and high accuracy results.

Unmanned Ground Vehicles for Continuous Crop Monitoring in Agriculture: Assessing the Readiness of Current ICT Technology

Continuous crop monitoring enables the early detection of field emergencies such as pests, diseases, and nutritional deficits, allowing for less invasive interventions and yielding economic, environmental, and health benefits. The work organization of modern agriculture, however, is not compatible with continuous human monitoring. ICT can facilitate this process using autonomous Unmanned Ground Vehicles (UGVs) to navigate crops, detect issues, georeference them, and report to human experts in real time. This review evaluates the current state of ICT technology to determine if it supports autonomous, continuous crop monitoring. The focus is on shifting from traditional cloud-based approaches, where data are sent to remote computers for deferred processing, to a hybrid design emphasizing edge computing for real-time analysis in the field. Key aspects considered include algorithms for in-field navigation, AIoT models for detecting agricultural emergencies, and advanced edge devices that are capable of managing sensors, collecting data, performing real-time deep learning inference, ensuring precise mapping and navigation, and sending alert reports with minimal human intervention. State-of-the-art research and development in this field suggest that general, not necessarily crop-specific, prototypes of fully autonomous UGVs for continuous monitoring are now at hand. Additionally, the demand for low-power consumption and affordable solutions can be practically addressed.

All‐Electronic Memristor Based on Charge Carrier Confinement in Bulk Semiconductor of Metal–Semiconductor–Metal Structure

AbstractMemristors have gained significant attention in recent years due to their potential applications in computing and memory technology by offering higher performance, lower power consumption, and increased storage capacity. In this paper, a new type of volatile memristor is presented by analyzing the dynamic behavior of charge carriers within a metal–semiconductor–metal (MSM) structure. It is shown that an all‐electronic memristor is achieved through the confinement of majority charge carriers within the bulk semiconductor by the favor of high barrier Schottky contacts. The findings reveal a remarkable current offset between forward and backward scans, along with exceptional current pulse consistency with a tunable current level using pulse frequency. These characteristics greatly simplify the process of designing electrical circuits incorporating this memristor variant. Furthermore, this research paves the way for the development of crystalline semiconductor‐based memristors. While various semiconductors with controllable doping densities can be considered as potential candidates for this type of memristor, the calculations using silicon demonstrate the integration of this semiconductor with the current technology holds significant promise for two terminal memristors.

A Neuromorphic Radar Sensor for Low-Power IoT Systems

As radar sensors become an integral component of Internet of Things (IoT) systems, the challenge of high power consumption poses a significant barrier, especially for battery-operated devices. This article introduces NeuroRadar, a groundbreaking solution that leverages a radar front-end capable of generating spike sequences, which can be efficiently processed by energy-saving Spiking Neural Networks (SNNs). We explore the innovative design and implementation of NeuroRadar, showcasing its effectiveness in applications like gesture recognition and human tracking. By achieving dramatically lower power consumption compared to traditional radar systems, NeuroRadar represents a new paradigm in energy-efficient IoT sensing.